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Berkant Savas is an Associate Professor at Linköping University with a research focus in scientific computing and numerical linear and multilinear algebra. His work emphasizes large-scale computations and their applications in diverse areas such as link prediction in dynamic networks and group recommendation systems. Savas has developed algorithms for tensor computations, including low multilinear rank approximations of tensors and Krylov-type methods. He is also involved in optimizing problems defined on Grassmann and Stiefel manifolds. Savas has authored publications, including an ACCEPTED article in the SIAM Journal Matrix Analysis Applications, and has created MATLAB implementations for clustered matrix approximations, which provide a fast and memory-efficient framework particularly suited for large sparse matrices. His MATLAB resources support researchers with numerous examples and user guides available for download. His contributions to the field are backed by a comprehensive portfolio of algorithms and frameworks that facilitate advanced computations in scientific research.
Requirements are standardized across the Faculty of Science and Engineering (Institute of Technology) for international Master's programs.